
import os
os.getcwd()

'C:\\Users\\Anoop Mishra'


#Importing required libraries

import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
import sklearn.metrics
from pylab import rcParams
%matplotlib inline
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)

# pandas version 0.24 or upper is required
pd.__version__

'1.1.3'


#load & view raw data
df = pd.read_csv('F:/insurance_claims.csv')
df.head(10)


df.describe()


df.dtypes

months_as_customer               int64
age                              int64
policy_number                    int64
policy_bind_date                object
policy_state                    object
policy_csl                      object
policy_deductable                int64
policy_annual_premium          float64
umbrella_limit                   int64
insured_zip                      int64
insured_sex                     object
insured_education_level         object
insured_occupation              object
insured_hobbies                 object
insured_relationship            object
capital-gains                    int64
capital-loss                     int64
incident_date                   object
incident_type                   object
collision_type                  object
incident_severity               object
authorities_contacted           object
incident_state                  object
incident_city                   object
incident_location               object
incident_hour_of_the_day         int64
number_of_vehicles_involved      int64
property_damage                 object
bodily_injuries                  int64
witnesses                        int64
police_report_available         object
total_claim_amount               int64
injury_claim                     int64
property_claim                   int64
vehicle_claim                    int64
auto_make                       object
auto_model                      object
auto_year                        int64
fraud_reported                  object
_c39                           float64
dtype: object


df.columns

Index(['months_as_customer', 'age', 'policy_number', 'policy_bind_date',
       'policy_state', 'policy_csl', 'policy_deductable',
       'policy_annual_premium', 'umbrella_limit', 'insured_zip', 'insured_sex',
       'insured_education_level', 'insured_occupation', 'insured_hobbies',
       'insured_relationship', 'capital-gains', 'capital-loss',
       'incident_date', 'incident_type', 'collision_type', 'incident_severity',
       'authorities_contacted', 'incident_state', 'incident_city',
       'incident_location', 'incident_hour_of_the_day',
       'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
       'witnesses', 'police_report_available', 'total_claim_amount',
       'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
       'auto_model', 'auto_year', 'fraud_reported', '_c39'],
      dtype='object')


df.shape

(1000, 40)


df.nunique()

months_as_customer              391
age                              46
policy_number                  1000
policy_bind_date                951
policy_state                      3
policy_csl                        3
policy_deductable                 3
policy_annual_premium           991
umbrella_limit                   11
insured_zip                     995
insured_sex                       2
insured_education_level           7
insured_occupation               14
insured_hobbies                  20
insured_relationship              6
capital-gains                   338
capital-loss                    354
incident_date                    60
incident_type                     4
collision_type                    4
incident_severity                 4
authorities_contacted             5
incident_state                    7
incident_city                     7
incident_location              1000
incident_hour_of_the_day         24
number_of_vehicles_involved       4
property_damage                   3
bodily_injuries                   3
witnesses                         4
police_report_available           3
total_claim_amount              763
injury_claim                    638
property_claim                  626
vehicle_claim                   726
auto_make                        14
auto_model                       39
auto_year                        21
fraud_reported                    2
_c39                              0
dtype: int64


plt.style.use('fivethirtyeight')
#ax = sns.distplot(df.age, bins=np.arange(19,64,5))
ax = sns.displot(df.age, bins=np.arange(19,64,5),kde=True)
#ax.set_ylabel('Density')
#ax.set_xlabel('Age')
plt.show()


np.seterr(invalid='ignore') # To remove "RuntimeWarning: invalid value encountered in minimum"
plt.style.use('fivethirtyeight')
ax = sns.countplot(x='fraud_reported', data=df, hue='fraud_reported')
ax.set_xlabel('Fraud Reported')
ax.set_ylabel('Fraud Count')
plt.show();


df['fraud_reported'].value_counts() # Count number of frauds vs non-frauds

N    753
Y    247
Name: fraud_reported, dtype: int64


df['incident_state'].value_counts()

NY    262
SC    248
WV    217
VA    110
NC    110
PA     30
OH     23
Name: incident_state, dtype: int64


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax = df.groupby('incident_state').fraud_reported.count().plot.bar(ylim=0)
ax.set_ylabel('Fraud Reported')
ax.set_xlabel('Incident State')
plt.show()


df['incident_state_count'] = df['incident_state']
for i in range(len(df['incident_state_count'])):
    if df.iloc[i, 40] == "NY":
        df.iloc[i, 40] = 262
    if df.iloc[i, 40] == "SC":
        df.iloc[i, 40] = 248
    if df.iloc[i, 40] == "WV":
        df.iloc[i, 40] = 217
    if df.iloc[i, 40] == "VA":
        df.iloc[i, 40] = 110
    if df.iloc[i, 40] == "NC":
        df.iloc[i, 40] = 110
    if df.iloc[i, 40] == "PA":
        df.iloc[i, 40] = 30
    if df.iloc[i, 40] == "OH":
        df.iloc[i, 40] = 23
        
from plotly.offline import plot
import plotly.graph_objs as go

data = [go.Choropleth(autocolorscale = True, locations = df['incident_state'],
                      z = df['incident_state_count'],
                      locationmode = 'USA-states',
                      marker = go.choropleth.Marker(line = go.choropleth.marker.Line(color = 'rgb(255,255,255)', width = 2)),
                      colorbar = go.choropleth.ColorBar(title = "Number of Incidents"))]
layout = go.Layout(
    title = go.layout.Title(
        text = 'Insurance Incident Claims on the Mid-Atlantic'
    ),
    geo = go.layout.Geo(
        scope = 'usa',
        projection = go.layout.geo.Projection(type = 'albers usa'),
        showlakes = True,
        lakecolor = 'rgb(255, 255, 255)'),
)
fig = go.Figure(data = data, layout = layout)

#plot(fig, filename = 'd3-cloropleth-map')  # for showing in seprate tab
fig.show()


plt.rcParams['figure.figsize'] = [15, 8]
ax= plt.style.use('fivethirtyeight')
table=pd.crosstab(df.age, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of Age vs Fraud Reported', fontsize=12)
plt.xlabel('Age')
plt.ylabel('Fraud Reported')
plt.show()


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(18,8))
ax = df.groupby('incident_date').total_claim_amount.count().plot.bar(ylim=0)
ax.set_ylabel('Claim amount ($)')
ax.set_xlabel('Incident Date')
plt.show()


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax = df.groupby('policy_state').fraud_reported.count().plot.bar(ylim=0)
ax.set_ylabel('Fraud Reported')
ax.set_xlabel('Policy State')
plt.show()


plt.rcParams['figure.figsize'] = [10, 6]
ax= plt.style.use('fivethirtyeight')
table=pd.crosstab(df.policy_state, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of Policy State vs Fraud Reported', fontsize=12)
plt.xlabel('Policy State')
plt.ylabel('Fraud Reported')
plt.show()


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax = df.groupby('incident_type').fraud_reported.count().plot.bar(ylim=0)
ax.set_xticklabels(ax.get_xticklabels(), rotation=20, ha="right")
ax.set_ylabel('Fraud Reported')
ax.set_xlabel('Incident Type')
plt.show()


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax = sns.countplot(x='incident_state', data=df)
ax.set_ylabel('Fraud Reported')
ax.set_xlabel('Incident State')

Text(0.5, 0, 'Incident State')


fig = plt.figure(figsize=(10,6))
ax = sns.countplot(y = 'insured_education_level', data=df) 
ax.set_ylabel('Policy Annual Premium')
ax.set_xlabel('Insured Education Level')
plt.show()

# # Breakdown of Average Vehicle claim by insured's education level, grouped by fraud reported


fig = plt.figure(figsize=(16,10))
ax = sns.catplot(x='fraud_reported', y='policy_annual_premium',hue='insured_education_level', data=df,
                    kind="bar", ci=None, palette="muted",height=6, legend=True, aspect=1.2) 

ax.set_axis_labels("Fraud Reported", "Policy Annual Premium")

plt.show()

<Figure size 1152x720 with 0 Axes>


plt.rcParams['figure.figsize'] = [14, 6]
table=pd.crosstab(df.insured_education_level, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of insured education vs Fraud reported', fontsize=12)
plt.xlabel('Insured Education Level')
plt.ylabel('Fraud Reported');


plt.rcParams['figure.figsize'] = [6, 6]
ax = (df['insured_sex'].value_counts()*100.0 /len(df))\
.plot.pie(autopct='%.1f%%', labels = ['Male', 'Female'], fontsize=12)                                                                           
ax.set_title('% Gender')
plt.ylabel('Insured Sex')
plt.show()


plt.rcParams['figure.figsize'] = [11, 6]
table=pd.crosstab(df.insured_sex, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of insured_sex vs Fraud', fontsize=12)
plt.xlabel('Insured Sex')
plt.ylabel('Fraud Reported')
plt.show()


plt.rcParams['figure.figsize'] = [8, 8]
ax = (df['insured_relationship'].value_counts()*100.0 /len(df))\
.plot.pie(autopct='%.1f%%', labels = ['husband', 'wife', 'own-child', 'unmarried', 'other-relative', 'not-in-family'],
         fontsize=12)                                                                           
ax.set_title('% Relationship')
plt.ylabel('Insured Relationship')
plt.show()


table=pd.crosstab(df.insured_relationship, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of insured_relationship vs Fraud', fontsize=12)
plt.xlabel('Insured Relationship')
plt.ylabel('Fraud Reported')
plt.show()


fig = plt.figure(figsize=(6,6))
ax = (df['incident_type'].value_counts()*100.0 /len(df))\
.plot.pie(autopct='%.1f%%', labels = ['Parked Car', 'Single Vehile Collision', 'Multi-vehicle Collision', 'Vehicle Theft'],
         fontsize=12);
plt.ylabel('Incident Type')

Text(0, 0.5, 'Incident Type')


fig = plt.figure(figsize=(6,6))
ax = (df['authorities_contacted'].value_counts()*100.0 /len(df))\
.plot.pie(autopct='%.1f%%', labels = ['Police', 'Fire', 'Other', 'None', 'Ambulance'],
         fontsize=12)
plt.ylabel('Authorities Contacted')

Text(0, 0.5, 'Authorities Contacted')


fig = plt.figure(figsize=(12,6))
ax = sns.countplot(x='auto_make', data=df)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.xlabel('Auto Make')
plt.ylabel('Auto Count')
plt.show()


fig = plt.figure(figsize=(6,6))
ax = (df['incident_severity'].value_counts()*100.0 /len(df))\
.plot.pie(autopct='%.1f%%', labels = ['Major Damage', 'Total Loss', 'Minor Damage', 'Trivial Damage'],
         fontsize=12)
plt.ylabel('Incident Severity');


fig = plt.figure(figsize=(10,6))
ax = sns.countplot(x='insured_hobbies', data=df)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.xlabel('Insured Hobbies')
plt.ylabel('Count of Insured')
plt.show()


df["insured_occupation"].value_counts()

machine-op-inspct    93
prof-specialty       85
tech-support         78
sales                76
exec-managerial      76
craft-repair         74
transport-moving     72
other-service        71
priv-house-serv      71
armed-forces         69
adm-clerical         65
protective-serv      63
handlers-cleaners    54
farming-fishing      53
Name: insured_occupation, dtype: int64


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax= df.groupby('auto_make').vehicle_claim.count().plot.bar(ylim=0)
ax.set_ylabel('Vehicle Claim')
ax.set_xlabel('Auto Make')
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.show()


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax= df.groupby('insured_hobbies').total_claim_amount.count().plot.bar(ylim=0)
ax.set_ylabel('Total Claim Amount')
ax.set_xlabel('Insured Hobbies')
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.show()


df['fraud_reported'].replace(to_replace='Y', value=1, inplace=True)
df['fraud_reported'].replace(to_replace='N',  value=0, inplace=True)

df.head()


df[['insured_zip']] = df[['insured_zip']].astype(object)
df.describe()


df.describe(include='all')


plt.style.use('fivethirtyeight')
plt.figure(figsize=(14,6))
table=pd.crosstab(df.policy_csl, df.fraud_reported)
table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)
plt.title('Stacked Bar Chart of Policy Csl vs Fraud', fontsize=12)
plt.xlabel('Policy Csl')
plt.ylabel('Fraud Reported')
plt.show();

<Figure size 1008x432 with 0 Axes>


df['csl_per_person'] = df.policy_csl.str.split('/', expand=True)[0]
df['csl_per_accident'] = df.policy_csl.str.split('/', expand=True)[1]
df['csl_per_person'].head()

0    250
1    250
2    100
3    250
4    500
Name: csl_per_person, dtype: object


df['csl_per_accident'].head()

0     500
1     500
2     300
3     500
4    1000
Name: csl_per_accident, dtype: object


df.auto_year.value_counts()  # check the spread of years to decide on further action.

1995    56
1999    55
2005    54
2011    53
2006    53
2007    52
2003    51
2010    50
2009    50
2013    49
2002    49
2015    47
1997    46
2012    46
2008    45
2014    44
2001    42
2000    42
1998    40
2004    39
1996    37
Name: auto_year, dtype: int64


df['vehicle_age'] = 2018 - df['auto_year'] # Deriving the age of the vehicle based on the year value 
df['vehicle_age'].head(10)

0    14
1    11
2    11
3     4
4     9
5    15
6     6
7     3
8     6
9    22
Name: vehicle_age, dtype: int64


bins = [-1, 3, 6, 9, 12, 17, 20, 24]  # Factorize according to the time period of the day.
names = ["past_midnight", "early_morning", "morning", 'fore-noon', 'afternoon', 'evening', 'night']
df['incident_period_of_day'] = pd.cut(df.incident_hour_of_the_day, bins, labels=names).astype(object)
df[['incident_hour_of_the_day', 'incident_period_of_day']].head(20)


# Check on categorical variables:
df.select_dtypes(include=['object']).columns  # checking categorcial columns

Index(['policy_bind_date', 'policy_state', 'policy_csl', 'insured_zip',
       'insured_sex', 'insured_education_level', 'insured_occupation',
       'insured_hobbies', 'insured_relationship', 'incident_date',
       'incident_type', 'collision_type', 'incident_severity',
       'authorities_contacted', 'incident_state', 'incident_city',
       'incident_location', 'property_damage', 'police_report_available',
       'auto_make', 'auto_model', 'incident_state_count', 'csl_per_person',
       'csl_per_accident', 'incident_period_of_day'],
      dtype='object')


# dropping unimportant columns

df = df.drop(columns = [
    'policy_number', 
    'policy_csl',
    'insured_zip',
    'policy_bind_date', 
    'incident_date', 
    'incident_location', 
    '_c39', 
    'auto_year', 
    'incident_hour_of_the_day'])

df.head(2)


# identify variables with '?' values
unknowns = {}
for i in list(df.columns):
    if (df[i]).dtype == object:
        j = np.sum(df[i] == "?")
        unknowns[i] = j
unknowns = pd.DataFrame.from_dict(unknowns, orient = 'index')
print(unknowns)

                           0
policy_state               0
insured_sex                0
insured_education_level    0
insured_occupation         0
insured_hobbies            0
insured_relationship       0
incident_type              0
collision_type           178
incident_severity          0
authorities_contacted      0
incident_state             0
incident_city              0
property_damage          360
police_report_available  343
auto_make                  0
auto_model                 0
incident_state_count       0
csl_per_person             0
csl_per_accident           0
incident_period_of_day     0


df.collision_type.value_counts()

Rear Collision     292
Side Collision     276
Front Collision    254
?                  178
Name: collision_type, dtype: int64


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax= df.groupby('collision_type').police_report_available.count().plot.bar(ylim=0)
ax.set_ylabel('Police Report')
ax.set_xlabel('Collision Type')
ax.set_xticklabels(ax.get_xticklabels(), rotation=10, ha="right")
plt.show()


df.property_damage.value_counts()

?      360
NO     338
YES    302
Name: property_damage, dtype: int64


plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(10,6))
ax= df.groupby('property_damage').police_report_available.count().plot.bar(ylim=0)
ax.set_ylabel('Police Report')
ax.set_xlabel('Property Damage')
ax.set_xticklabels(ax.get_xticklabels(), rotation=10, ha="right")
plt.show()


df.police_report_available.value_counts()

?      343
NO     343
YES    314
Name: police_report_available, dtype: int64


df.columns

Index(['months_as_customer', 'age', 'policy_state', 'policy_deductable',
       'policy_annual_premium', 'umbrella_limit', 'insured_sex',
       'insured_education_level', 'insured_occupation', 'insured_hobbies',
       'insured_relationship', 'capital-gains', 'capital-loss',
       'incident_type', 'collision_type', 'incident_severity',
       'authorities_contacted', 'incident_state', 'incident_city',
       'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
       'witnesses', 'police_report_available', 'total_claim_amount',
       'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
       'auto_model', 'fraud_reported', 'incident_state_count',
       'csl_per_person', 'csl_per_accident', 'vehicle_age',
       'incident_period_of_day'],
      dtype='object')


df._get_numeric_data().head()  # Checking numeric columns


df._get_numeric_data().columns

Index(['months_as_customer', 'age', 'policy_deductable',
       'policy_annual_premium', 'umbrella_limit', 'capital-gains',
       'capital-loss', 'number_of_vehicles_involved', 'bodily_injuries',
       'witnesses', 'total_claim_amount', 'injury_claim', 'property_claim',
       'vehicle_claim', 'fraud_reported', 'vehicle_age'],
      dtype='object')


df.select_dtypes(include=['object']).columns  # checking categorcial columns

Index(['policy_state', 'insured_sex', 'insured_education_level',
       'insured_occupation', 'insured_hobbies', 'insured_relationship',
       'incident_type', 'collision_type', 'incident_severity',
       'authorities_contacted', 'incident_state', 'incident_city',
       'property_damage', 'police_report_available', 'auto_make', 'auto_model',
       'incident_state_count', 'csl_per_person', 'csl_per_accident',
       'incident_period_of_day'],
      dtype='object')


dummies = pd.get_dummies(df[[
    'policy_state', 
    'insured_sex', 
    'insured_education_level',
    'insured_occupation', 
    'insured_hobbies', 
    'insured_relationship',
    'incident_type', 
    'incident_severity',
    'authorities_contacted', 
    'incident_state', 
    'incident_city',
    'auto_make', 
    'auto_model', 
    'csl_per_person', 
    'csl_per_accident',
    'incident_period_of_day']])

dummies = dummies.join(df[[
    'collision_type', 
    'property_damage', 
    'police_report_available', 
    "fraud_reported"]])

dummies.head()


X = dummies.iloc[:, 0:-1]  # predictor variables
y = dummies.iloc[:, -1]  # target variable

len(X.columns)

148


X.head(2)


y.head()

0    1
1    1
2    0
3    1
4    0
Name: fraud_reported, dtype: int64


from sklearn.preprocessing import LabelEncoder
X['collision_en'] = LabelEncoder().fit_transform(dummies['collision_type'])
X[['collision_type', 'collision_en']]


X['property_damage'].replace(to_replace='YES', value=1, inplace=True)
X['property_damage'].replace(to_replace='NO', value=0, inplace=True)
X['property_damage'].replace(to_replace='?', value=0, inplace=True)
X['police_report_available'].replace(to_replace='YES', value=1, inplace=True)
X['police_report_available'].replace(to_replace='NO', value=0, inplace=True)
X['police_report_available'].replace(to_replace='?', value=0, inplace=True)

X.head(10)


X = X.drop(columns = ['collision_type'])
X.head(2)


X = pd.concat([X, df._get_numeric_data()], axis=1)  # joining numeric columns
X.head(2)


X.columns

Index(['policy_state_IL', 'policy_state_IN', 'policy_state_OH',
       'insured_sex_FEMALE', 'insured_sex_MALE',
       'insured_education_level_Associate', 'insured_education_level_College',
       'insured_education_level_High School', 'insured_education_level_JD',
       'insured_education_level_MD',
       ...
       'capital-loss', 'number_of_vehicles_involved', 'bodily_injuries',
       'witnesses', 'total_claim_amount', 'injury_claim', 'property_claim',
       'vehicle_claim', 'fraud_reported', 'vehicle_age'],
      dtype='object', length=164)


X = X.drop(columns = ['fraud_reported'])  # dropping target variable 'fraud_reported'
X.columns

Index(['policy_state_IL', 'policy_state_IN', 'policy_state_OH',
       'insured_sex_FEMALE', 'insured_sex_MALE',
       'insured_education_level_Associate', 'insured_education_level_College',
       'insured_education_level_High School', 'insured_education_level_JD',
       'insured_education_level_MD',
       ...
       'capital-gains', 'capital-loss', 'number_of_vehicles_involved',
       'bodily_injuries', 'witnesses', 'total_claim_amount', 'injury_claim',
       'property_claim', 'vehicle_claim', 'vehicle_age'],
      dtype='object', length=163)


from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

# evaluate an LDA model on the dataset using k-fold cross validation
model = LinearDiscriminantAnalysis()
kfold = KFold(n_splits=5, random_state=7)
result = cross_val_score(model, X, y, cv=kfold, scoring='accuracy')
print(result.mean())

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:552: FitFailedWarning:

Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py", line 464, in fit
    self._solve_svd(X, y)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py", line 381, in _solve_svd
    U, S, V = linalg.svd(X, full_matrices=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\linalg\decomp_svd.py", line 132, in svd
    raise LinAlgError("SVD did not converge")
numpy.linalg.LinAlgError: SVD did not converge

nan


print("Accuracy: %0.2f (+/- %0.2f)" % (result.mean(), result.std() * 2))

Accuracy: nan (+/- nan)


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=7)
print('length of X_train and X_test: ', len(X_train), len(X_test))
print('length of y_train and y_test: ', len(y_train), len(y_test))

length of X_train and X_test:  800 200
length of y_train and y_test:  800 200


from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, recall_score, classification_report, cohen_kappa_score
from sklearn import metrics 

# Baseline Random forest based Model
rfc = RandomForestClassifier(n_estimators=200)
    
kfold = KFold(n_splits=5, random_state=7)
result2 = cross_val_score(rfc, X_train, y_train, cv=kfold, scoring='accuracy')
print(result2.mean())

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

0.765


# Generate a Histogram plot for anomaly detection
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = [15, 8]
df.plot(kind='hist')
plt.show()


plt.rcParams['figure.figsize'] = [5, 5]
sns.boxplot(x=X.policy_annual_premium)
plt.xlabel('Policy Annual Premium')
plt.show()


plt.rcParams['figure.figsize'] = [5, 5]
sns.boxplot(x=X.witnesses)
plt.xlabel('Witnesses')
plt.show()


plt.rcParams['figure.figsize'] = [5, 5]
sns.boxplot(x=X.vehicle_age)
plt.xlabel('Vehicle Age')
plt.show()


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler(with_mean=False)
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

X_train_scaled

array([[0.        , 0.        , 2.08816082, ..., 0.90375361, 1.84093195,
        1.81430422],
       [0.        , 0.        , 2.08816082, ..., 2.10117545, 1.60502305,
        2.14417772],
       [0.        , 2.16950399, 0.        , ..., 2.69678424, 2.74665362,
        0.82468374],
       ...,
       [2.11480423, 0.        , 0.        , ..., 1.26980485, 2.58657258,
        3.7935452 ],
       [0.        , 2.16950399, 0.        , ..., 0.10960856, 0.22327092,
        2.80392471],
       [0.        , 2.16950399, 0.        , ..., 3.29239303, 2.51495738,
        1.15455723]])


X_train_scaled = pd.DataFrame(X_train_scaled, columns = X_train.columns) # retaining columns names
X_train_scaled.head(2)


# Generate a Histogram plot on scaled data to check anomalies
plt.rcParams['figure.figsize'] = [15, 8]
X_train_scaled.plot(kind='hist')

<AxesSubplot:ylabel='Frequency'>


x_train_scaled = pd.DataFrame.to_numpy(X_train_scaled) # converting to array for computational ease
x_train_scaled

array([[0.        , 0.        , 2.08816082, ..., 0.90375361, 1.84093195,
        1.81430422],
       [0.        , 0.        , 2.08816082, ..., 2.10117545, 1.60502305,
        2.14417772],
       [0.        , 2.16950399, 0.        , ..., 2.69678424, 2.74665362,
        0.82468374],
       ...,
       [2.11480423, 0.        , 0.        , ..., 1.26980485, 2.58657258,
        3.7935452 ],
       [0.        , 2.16950399, 0.        , ..., 0.10960856, 0.22327092,
        2.80392471],
       [0.        , 2.16950399, 0.        , ..., 3.29239303, 2.51495738,
        1.15455723]])


from sklearn.ensemble import AdaBoostClassifier, VotingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn import model_selection
from sklearn.model_selection import KFold, cross_val_score
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC

xgb = XGBClassifier()
logreg= LogisticRegressionCV(solver='lbfgs', cv=10)
knn = KNeighborsClassifier(5)
svcl = SVC()
adb = AdaBoostClassifier()
dt = DecisionTreeClassifier(max_depth=5)
rf = RandomForestClassifier()
lda = LinearDiscriminantAnalysis()
gnb = GaussianNB()

# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = []
models.append(('LR', LogisticRegressionCV(solver='lbfgs', max_iter=5000, cv=10)))
models.append(('XGB', XGBClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('DT', DecisionTreeClassifier()))
models.append(('SVM', SVC(gamma='auto')))
models.append(('RF', RandomForestClassifier(n_estimators=200)))
models.append(('ADA', AdaBoostClassifier(n_estimators=200)))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
              
# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'
for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, x_train_scaled, y_train, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

# boxplot algorithm comparison
plt.rcParams['figure.figsize'] = [15, 8]              
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

LR: 0.826250 (0.034664)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

XGB: 0.828750 (0.023083)
KNN: 0.735000 (0.055283)
DT: 0.795000 (0.029686)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

SVM: 0.780000 (0.038810)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

RF: 0.797500 (0.047697)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

ADA: 0.797500 (0.036142)


clf1= LogisticRegressionCV(solver='lbfgs', max_iter=5000, cv=10)
clf2 = XGBClassifier() 

clf = [
    ('LR', clf1), 
    ('XGB', clf2)] 
    
#create our voting classifier, inputting our models
eclf= VotingClassifier(estimators=[
    ('LR', clf1), 
    ('XGB', clf2)], voting='hard')

for clf, label in zip([clf1, clf2, eclf], [
    'Logistic Regression', 
    'XGB Classifier',
    'Ensemble']):
    
    scores = cross_val_score(clf, x_train_scaled, y_train, cv=10, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))

Accuracy: 0.83 (+/- 0.03) [Logistic Regression]
Accuracy: 0.83 (+/- 0.02) [XGB Classifier]
Accuracy: 0.82 (+/- 0.03) [Ensemble]


from numpy import sort
from sklearn.feature_selection import SelectFromModel

# fit model on all training data
xgb = XGBClassifier()
xgb.fit(x_train_scaled, y_train)

# make predictions for test data and evaluate
xgb_pred = xgb.predict(X_test_scaled)
predictions = [round(value) for value in xgb_pred]
accuracy = accuracy_score(y_test, xgb_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

# Fit model using each importance as a threshold
thresholds = sort(xgb.feature_importances_)
for thresh in thresholds:
    
    # select features using threshold
    selection = SelectFromModel(xgb, threshold=thresh, prefit=True)
    select_X_train = selection.transform(x_train_scaled)
    
    # train model
    selection_model = XGBClassifier()
    selection_model.fit(select_X_train, y_train)
    
    # eval model
    select_X_test = selection.transform(X_test_scaled)
    xgb_pred = selection_model.predict(select_X_test)
    predictions = [round(value) for value in xgb_pred]
    accuracy = accuracy_score(y_test, xgb_pred)
    print("Thresh=%.3f, n=%d, Accuracy: %.2f%%" % (thresh, select_X_train.shape[1], accuracy*100.0))

Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.000, n=163, Accuracy: 79.50%
Thresh=0.002, n=73, Accuracy: 79.50%
Thresh=0.004, n=72, Accuracy: 79.50%
Thresh=0.004, n=71, Accuracy: 79.50%
Thresh=0.004, n=70, Accuracy: 79.50%
Thresh=0.005, n=69, Accuracy: 80.00%
Thresh=0.005, n=68, Accuracy: 79.50%
Thresh=0.005, n=67, Accuracy: 79.50%
Thresh=0.005, n=66, Accuracy: 80.50%
Thresh=0.005, n=65, Accuracy: 80.50%
Thresh=0.006, n=64, Accuracy: 80.00%
Thresh=0.006, n=63, Accuracy: 80.00%
Thresh=0.006, n=62, Accuracy: 81.00%
Thresh=0.006, n=61, Accuracy: 79.50%
Thresh=0.006, n=60, Accuracy: 79.50%
Thresh=0.007, n=59, Accuracy: 80.50%
Thresh=0.007, n=58, Accuracy: 80.00%
Thresh=0.007, n=57, Accuracy: 80.00%
Thresh=0.007, n=56, Accuracy: 80.50%
Thresh=0.007, n=55, Accuracy: 79.00%
Thresh=0.007, n=54, Accuracy: 79.00%
Thresh=0.007, n=53, Accuracy: 81.00%
Thresh=0.007, n=52, Accuracy: 79.50%
Thresh=0.008, n=51, Accuracy: 80.50%
Thresh=0.008, n=50, Accuracy: 80.50%
Thresh=0.008, n=49, Accuracy: 80.50%
Thresh=0.009, n=48, Accuracy: 82.00%
Thresh=0.009, n=47, Accuracy: 82.00%
Thresh=0.009, n=46, Accuracy: 82.00%
Thresh=0.010, n=45, Accuracy: 80.50%
Thresh=0.010, n=44, Accuracy: 80.50%
Thresh=0.010, n=43, Accuracy: 81.00%
Thresh=0.010, n=42, Accuracy: 82.00%
Thresh=0.010, n=41, Accuracy: 82.50%
Thresh=0.010, n=40, Accuracy: 80.00%
Thresh=0.010, n=39, Accuracy: 80.00%
Thresh=0.010, n=38, Accuracy: 80.00%
Thresh=0.010, n=37, Accuracy: 82.00%
Thresh=0.011, n=36, Accuracy: 81.00%
Thresh=0.011, n=35, Accuracy: 80.00%
Thresh=0.011, n=34, Accuracy: 78.00%
Thresh=0.011, n=33, Accuracy: 78.00%
Thresh=0.011, n=32, Accuracy: 79.50%
Thresh=0.011, n=31, Accuracy: 78.00%
Thresh=0.011, n=30, Accuracy: 78.00%
Thresh=0.011, n=29, Accuracy: 80.50%
Thresh=0.012, n=28, Accuracy: 81.50%
Thresh=0.012, n=27, Accuracy: 81.50%
Thresh=0.012, n=26, Accuracy: 82.00%
Thresh=0.012, n=25, Accuracy: 80.50%
Thresh=0.012, n=24, Accuracy: 79.50%
Thresh=0.013, n=23, Accuracy: 79.00%
Thresh=0.013, n=22, Accuracy: 79.00%
Thresh=0.013, n=21, Accuracy: 80.00%
Thresh=0.013, n=20, Accuracy: 79.50%
Thresh=0.013, n=19, Accuracy: 81.00%
Thresh=0.013, n=18, Accuracy: 80.00%
Thresh=0.013, n=17, Accuracy: 81.00%
Thresh=0.014, n=16, Accuracy: 80.00%
Thresh=0.014, n=15, Accuracy: 79.50%
Thresh=0.015, n=14, Accuracy: 81.50%
Thresh=0.016, n=13, Accuracy: 81.00%
Thresh=0.016, n=12, Accuracy: 82.00%
Thresh=0.017, n=11, Accuracy: 81.50%
Thresh=0.018, n=10, Accuracy: 82.00%
Thresh=0.018, n=9, Accuracy: 82.00%
Thresh=0.018, n=8, Accuracy: 83.00%
Thresh=0.018, n=7, Accuracy: 82.00%
Thresh=0.018, n=6, Accuracy: 83.50%
Thresh=0.019, n=5, Accuracy: 82.50%
Thresh=0.024, n=4, Accuracy: 82.50%
Thresh=0.057, n=3, Accuracy: 83.00%
Thresh=0.079, n=2, Accuracy: 81.00%
Thresh=0.136, n=1, Accuracy: 79.00%


from xgboost import plot_importance
x = XGBClassifier()
x.fit(X_train_scaled, y_train) # fitting the model again on dataframe to identify the feature names

plt.rcParams['figure.figsize'] = [25, 20]
# plot feature importance
plot_importance(x);


from pprint import pprint
# Check parameters used 
print('Parameters currently in use:\n')
pprint(x.get_params())

Parameters currently in use:

{'base_score': 0.5,
 'booster': 'gbtree',
 'colsample_bylevel': 1,
 'colsample_bynode': 1,
 'colsample_bytree': 1,
 'gamma': 0,
 'learning_rate': 0.1,
 'max_delta_step': 0,
 'max_depth': 3,
 'min_child_weight': 1,
 'missing': None,
 'n_estimators': 100,
 'n_jobs': 1,
 'nthread': None,
 'objective': 'binary:logistic',
 'random_state': 0,
 'reg_alpha': 0,
 'reg_lambda': 1,
 'scale_pos_weight': 1,
 'seed': None,
 'silent': None,
 'subsample': 1,
 'verbosity': 1}


from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
plt.rcParams['figure.figsize'] = [10, 6]

# grid search
max_depth = range(1, 11, 2)
print(max_depth)

param_grid = dict(max_depth=max_depth)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)
grid_search = GridSearchCV(xgb, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold, verbose=1,  iid=False)
grid_result = grid_search.fit(x_train_scaled, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))


# plot
pyplot.errorbar(max_depth, means, yerr=stds)
pyplot.title("XGBoost max_depth vs Log Loss")
pyplot.xlabel('max_depth')
pyplot.ylabel('Log Loss')

range(1, 11, 2)
Fitting 10 folds for each of 5 candidates, totalling 50 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   14.7s
[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   18.1s finished
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:849: FutureWarning:

The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.

Best: -0.368020 using {'max_depth': 1}
-0.368020 (0.058795) with: {'max_depth': 1}
-0.383396 (0.068880) with: {'max_depth': 3}
-0.417851 (0.102162) with: {'max_depth': 5}
-0.436605 (0.118897) with: {'max_depth': 7}
-0.455016 (0.125771) with: {'max_depth': 9}

Text(0, 0.5, 'Log Loss')


import numpy

n_estimators = [50, 100, 150, 200]
max_depth = [2, 4, 6, 8]
print(max_depth)
param_grid = dict(max_depth=max_depth, n_estimators=n_estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)
grid_search = GridSearchCV(xgb, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold, verbose=1, iid=False)
grid_result = grid_search.fit(x_train_scaled, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

# plot results
scores = numpy.array(means).reshape(len(max_depth), len(n_estimators))
for i, value in enumerate(max_depth):
    pyplot.plot(n_estimators, scores[i], label='depth: ' + str(value))
pyplot.legend()
pyplot.xlabel('n_estimators')
pyplot.ylabel('Log Loss')

[2, 4, 6, 8]
Fitting 10 folds for each of 16 candidates, totalling 160 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    8.6s
[Parallel(n_jobs=-1)]: Done 160 out of 160 | elapsed:  1.1min finished
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:849: FutureWarning:

The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.

Best: -0.357098 using {'max_depth': 2, 'n_estimators': 50}
-0.357098 (0.056692) with: {'max_depth': 2, 'n_estimators': 50}
-0.373042 (0.068481) with: {'max_depth': 2, 'n_estimators': 100}
-0.385426 (0.074841) with: {'max_depth': 2, 'n_estimators': 150}
-0.392391 (0.078139) with: {'max_depth': 2, 'n_estimators': 200}
-0.372726 (0.073106) with: {'max_depth': 4, 'n_estimators': 50}
-0.410393 (0.094496) with: {'max_depth': 4, 'n_estimators': 100}
-0.437988 (0.113429) with: {'max_depth': 4, 'n_estimators': 150}
-0.466359 (0.127664) with: {'max_depth': 4, 'n_estimators': 200}
-0.386799 (0.090680) with: {'max_depth': 6, 'n_estimators': 50}
-0.439307 (0.126254) with: {'max_depth': 6, 'n_estimators': 100}
-0.480956 (0.142816) with: {'max_depth': 6, 'n_estimators': 150}
-0.510842 (0.159092) with: {'max_depth': 6, 'n_estimators': 200}
-0.395697 (0.101834) with: {'max_depth': 8, 'n_estimators': 50}
-0.449124 (0.133029) with: {'max_depth': 8, 'n_estimators': 100}
-0.483625 (0.150359) with: {'max_depth': 8, 'n_estimators': 150}
-0.502735 (0.159152) with: {'max_depth': 8, 'n_estimators': 200}

Text(0, 0.5, 'Log Loss')


xgb = XGBClassifier(objective='binary:logistic', random_state=7, n_jobs=-1)
xgb.fit(x_train_scaled, y_train)
scores = cross_val_score(xgb, x_train_scaled, y_train, cv=kfold, scoring='brier_score_loss')
print('Brier loss:', "{0:.5f}".format(np.mean(scores)*-1))

Brier loss: 0.11885


print(xgb.get_params())

{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 1, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 3, 'min_child_weight': 1, 'missing': None, 'n_estimators': 100, 'n_jobs': -1, 'nthread': None, 'objective': 'binary:logistic', 'random_state': 7, 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': None, 'silent': None, 'subsample': 1, 'verbosity': 1}


from sklearn.model_selection import RandomizedSearchCV

# Create the parameter grid
params = {
    'learning_rate': [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3],
    'n_estimators': [int(x) for x in np.linspace(start=100, stop=500, num=9)],
    'max_depth': [i for i in range(3, 10)],
    'min_child_weight': [i for i in range(1, 7)],
    'subsample': [i/10.0 for i in range(6,11)],
    'colsample_bytree': [i/10.0 for i in range(6,11)]
}
 
# Create the randomised grid search model
# "n_iter = number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution"
rgs = RandomizedSearchCV(estimator=xgb, param_distributions=params, n_iter=200, cv=kfold, 
                         random_state=7, n_jobs=-1,
                         scoring='brier_score_loss', return_train_score=True)
# Fit rgs
rgs.fit(x_train_scaled, y_train)
 
# Print results
print(rgs)

RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=7, shuffle=True),
                   estimator=XGBClassifier(n_jobs=-1, random_state=7),
                   n_iter=200, n_jobs=-1,
                   param_distributions={'colsample_bytree': [0.6, 0.7, 0.8, 0.9,
                                                             1.0],
                                        'learning_rate': [0.0001, 0.001, 0.01,
                                                          0.1, 0.2, 0.3],
                                        'max_depth': [3, 4, 5, 6, 7, 8, 9],
                                        'min_child_weight': [1, 2, 3, 4, 5, 6],
                                        'n_estimators': [100, 150, 200, 250,
                                                         300, 350, 400, 450,
                                                         500],
                                        'subsample': [0.6, 0.7, 0.8, 0.9, 1.0]},
                   random_state=7, return_train_score=True,
                   scoring='brier_score_loss')


best_score = rgs.best_score_
best_params = rgs.best_params_
print("Best score: {}".format(best_score))
print("Best params: ")
for param_name in sorted(best_params.keys()):
    print('%s: %r' % (param_name, best_params[param_name]))

Best score: -0.10631241913018777
Best params: 
colsample_bytree: 1.0
learning_rate: 0.01
max_depth: 3
min_child_weight: 3
n_estimators: 200
subsample: 1.0


# make predictions for test data and evaluate
rgs_pred = rgs.predict(X_test_scaled)

print('Accuracy: ', round(accuracy_score(y_test, rgs_pred)*100, 2))
print( 'Cohen Kappa: '+ str(np.round(cohen_kappa_score(y_test, rgs_pred),3)))
print('Recall: ', round(recall_score(y_test, rgs_pred)*100, 2))
print('\n Classification Report:\n', classification_report(y_test, rgs_pred))

print(result.mean())

Accuracy:  82.0
Cohen Kappa: 0.58
Recall:  84.31

 Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.81      0.87       149
           1       0.61      0.84      0.70        51

    accuracy                           0.82       200
   macro avg       0.77      0.83      0.79       200
weighted avg       0.85      0.82      0.83       200

nan


xgb = XGBClassifier()

# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = []
models.append(('XGB', XGBClassifier()))
              
# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'
for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, x_train_scaled, y_train, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

XGB: 0.828750 (0.023083)


# Fit rgs
model.fit(x_train_scaled, y_train)

# make predictions for test data
y_pred = model.predict(X_test_scaled)
predictions = [round(value) for value in y_pred]

# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

Accuracy: 79.50%


from sklearn.metrics import average_precision_score
average_precision = average_precision_score(y_test, rgs_pred)

print('Average precision-recall score: {0:0.2f}'.format(
      average_precision))

Average precision-recall score: 0.55


from sklearn.metrics import precision_recall_curve
from inspect import signature

plt.rcParams['figure.figsize'] = [10, 6]

precision, recall, _ = precision_recall_curve(y_test, rgs_pred)

step_kwargs = ({'step': 'post'}
               if 'step' in signature(plt.fill_between).parameters
               else {})
plt.step(recall, precision, color='b', alpha=0.2,where='post')
plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall curve: AP={0:0.2f}'.format(average_precision), fontsize=12)

Text(0.5, 1.0, 'Precision-Recall curve: AP=0.55')


from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score

# calculate AUC
auc = roc_auc_score(y_test, rgs_pred)
print('AUC: %.3f' % auc)

# calculate roc curve
fpr, tpr, thresholds = roc_curve(y_test, rgs_pred)

# plot no skill
plt.rcParams['figure.figsize'] = [10, 6]
plt.plot([0, 1], [0, 1], linestyle='--')

# plot the roc curve for the model
plt.plot(fpr, tpr, marker='.')

AUC: 0.828

[<matplotlib.lines.Line2D at 0x2066ce84748>]


from sklearn.metrics import confusion_matrix
import itertools

#Evaluation of Model - Confusion Matrix Plot
def plot_confusion_matrix(cm, classes, title ='Confusion matrix', normalize = False, cmap = plt.cm.Blues):
    
    print('Confusion matrix')

    print(cm)
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure(figsize=(10,6))

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=40)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()


# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, rgs_pred)
np.set_printoptions(precision=2)

# Plot confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=['Fraud_Y','Fraud_N'],
                      title='Confusion matrix')

Confusion matrix
[[121  28]
 [  8  43]]

<Figure size 720x432 with 0 Axes>


from sklearn.feature_selection import VarianceThreshold

constant_filter = VarianceThreshold(threshold=0.057) 
constant_filter.fit(X_train_scaled)  

constant_columns = [column for column in X_train_scaled.columns  
                    if column not in X_train_scaled.columns[constant_filter.get_support()]]

print(len(constant_columns))

0


correlated_features = set()  
correlation_matrix = X_train_scaled.corr()  

for i in range(len(correlation_matrix .columns)):  
    for j in range(i):
        if abs(correlation_matrix.iloc[i, j]) > 0.8:
            colname = correlation_matrix.columns[i]
            correlated_features.add(colname)
            
len(correlated_features)

10


print(correlated_features)

{'injury_claim', 'insured_sex_MALE', 'csl_per_accident_300', 'csl_per_accident_1000', 'number_of_vehicles_involved', 'age', 'csl_per_accident_500', 'auto_model_Wrangler', 'property_claim', 'vehicle_claim'}


X.head(1)


x = X.drop([ 
       'vehicle_claim', 
        'injury_claim', 
        'age', 
        'csl_per_accident_500', 
        'csl_per_accident_1000', 
        'auto_model_Wrangler', 
        'insured_sex_MALE', 
        'csl_per_accident_300', 
        'property_claim', 
        'number_of_vehicles_involved'], axis=1)

x.head(1)


x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=7)
print('length of X_train and X_test: ', len(x_train), len(x_test))
print('length of y_train and y_test: ', len(y_train), len(y_test))

length of X_train and X_test:  800 200
length of y_train and y_test:  800 200


a_train_scaled = scaler.fit_transform(x_train)
a_test_scaled = scaler.transform(x_test)


xgb = XGBClassifier()
logreg= LogisticRegressionCV(solver='lbfgs', cv=10)

# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = []
models.append(('LR', LogisticRegressionCV(solver='lbfgs', max_iter=5000, cv=10)))
models.append(('XGB', XGBClassifier()))
              
# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'
for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, a_train_scaled, y_train, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

LR: 0.822500 (0.037417)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:297: FutureWarning:

Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.

XGB: 0.820000 (0.033166)

	months_as_customer	age	policy_number	policy_bind_date	policy_state	policy_csl	policy_deductable	policy_annual_premium	umbrella_limit	insured_zip	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-gains	capital-loss	incident_date	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	incident_location	incident_hour_of_the_day	number_of_vehicles_involved	property_damage	bodily_injuries	witnesses	police_report_available	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_make	auto_model	auto_year	fraud_reported	_c39
0	328	48	521585	17-10-2014	OH	250/500	1000	1406.91	0	466132	MALE	MD	craft-repair	sleeping	husband	53300	0	25-01-2015	Single Vehicle Collision	Side Collision	Major Damage	Police	SC	Columbus	9935 4th Drive	5	1	YES	1	2	YES	71610	6510	13020	52080	Saab	92x	2004	Y	NaN
1	228	42	342868	27-06-2006	IN	250/500	2000	1197.22	5000000	468176	MALE	MD	machine-op-inspct	reading	other-relative	0	0	21-01-2015	Vehicle Theft	?	Minor Damage	Police	VA	Riverwood	6608 MLK Hwy	8	1	?	0	0	?	5070	780	780	3510	Mercedes	E400	2007	Y	NaN
2	134	29	687698	06-09-2000	OH	100/300	2000	1413.14	5000000	430632	FEMALE	PhD	sales	board-games	own-child	35100	0	22-02-2015	Multi-vehicle Collision	Rear Collision	Minor Damage	Police	NY	Columbus	7121 Francis Lane	7	3	NO	2	3	NO	34650	7700	3850	23100	Dodge	RAM	2007	N	NaN
3	256	41	227811	25-05-1990	IL	250/500	2000	1415.74	6000000	608117	FEMALE	PhD	armed-forces	board-games	unmarried	48900	-62400	10-01-2015	Single Vehicle Collision	Front Collision	Major Damage	Police	OH	Arlington	6956 Maple Drive	5	1	?	1	2	NO	63400	6340	6340	50720	Chevrolet	Tahoe	2014	Y	NaN
4	228	44	367455	06-06-2014	IL	500/1000	1000	1583.91	6000000	610706	MALE	Associate	sales	board-games	unmarried	66000	-46000	17-02-2015	Vehicle Theft	?	Minor Damage	None	NY	Arlington	3041 3rd Ave	20	1	NO	0	1	NO	6500	1300	650	4550	Accura	RSX	2009	N	NaN
5	256	39	104594	12-10-2006	OH	250/500	1000	1351.10	0	478456	FEMALE	PhD	tech-support	bungie-jumping	unmarried	0	0	02-01-2015	Multi-vehicle Collision	Rear Collision	Major Damage	Fire	SC	Arlington	8973 Washington St	19	3	NO	0	2	NO	64100	6410	6410	51280	Saab	95	2003	Y	NaN
6	137	34	413978	04-06-2000	IN	250/500	1000	1333.35	0	441716	MALE	PhD	prof-specialty	board-games	husband	0	-77000	13-01-2015	Multi-vehicle Collision	Front Collision	Minor Damage	Police	NY	Springfield	5846 Weaver Drive	0	3	?	0	0	?	78650	21450	7150	50050	Nissan	Pathfinder	2012	N	NaN
7	165	37	429027	03-02-1990	IL	100/300	1000	1137.03	0	603195	MALE	Associate	tech-support	base-jumping	unmarried	0	0	27-02-2015	Multi-vehicle Collision	Front Collision	Total Loss	Police	VA	Columbus	3525 3rd Hwy	23	3	?	2	2	YES	51590	9380	9380	32830	Audi	A5	2015	N	NaN
8	27	33	485665	05-02-1997	IL	100/300	500	1442.99	0	601734	FEMALE	PhD	other-service	golf	own-child	0	0	30-01-2015	Single Vehicle Collision	Front Collision	Total Loss	Police	WV	Arlington	4872 Rock Ridge	21	1	NO	1	1	YES	27700	2770	2770	22160	Toyota	Camry	2012	N	NaN
9	212	42	636550	25-07-2011	IL	100/300	500	1315.68	0	600983	MALE	PhD	priv-house-serv	camping	wife	0	-39300	05-01-2015	Single Vehicle Collision	Rear Collision	Total Loss	Other	NC	Hillsdale	3066 Francis Ave	14	1	NO	2	1	?	42300	4700	4700	32900	Saab	92x	1996	N	NaN

	months_as_customer	age	policy_number	policy_deductable	policy_annual_premium	umbrella_limit	insured_zip	capital-gains	capital-loss	incident_hour_of_the_day	number_of_vehicles_involved	bodily_injuries	witnesses	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_year	_c39
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1.000000e+03	1000.000000	1000.000000	1000.000000	1000.000000	1000.00000	1000.000000	1000.000000	1000.00000	1000.000000	1000.000000	1000.000000	1000.000000	0.0
mean	203.954000	38.948000	546238.648000	1136.000000	1256.406150	1.101000e+06	501214.488000	25126.100000	-26793.700000	11.644000	1.83900	0.992000	1.487000	52761.94000	7433.420000	7399.570000	37928.950000	2005.103000	NaN
std	115.113174	9.140287	257063.005276	611.864673	244.167395	2.297407e+06	71701.610941	27872.187708	28104.096686	6.951373	1.01888	0.820127	1.111335	26401.53319	4880.951853	4824.726179	18886.252893	6.015861	NaN
min	0.000000	19.000000	100804.000000	500.000000	433.330000	-1.000000e+06	430104.000000	0.000000	-111100.000000	0.000000	1.00000	0.000000	0.000000	100.00000	0.000000	0.000000	70.000000	1995.000000	NaN
25%	115.750000	32.000000	335980.250000	500.000000	1089.607500	0.000000e+00	448404.500000	0.000000	-51500.000000	6.000000	1.00000	0.000000	1.000000	41812.50000	4295.000000	4445.000000	30292.500000	2000.000000	NaN
50%	199.500000	38.000000	533135.000000	1000.000000	1257.200000	0.000000e+00	466445.500000	0.000000	-23250.000000	12.000000	1.00000	1.000000	1.000000	58055.00000	6775.000000	6750.000000	42100.000000	2005.000000	NaN
75%	276.250000	44.000000	759099.750000	2000.000000	1415.695000	0.000000e+00	603251.000000	51025.000000	0.000000	17.000000	3.00000	2.000000	2.000000	70592.50000	11305.000000	10885.000000	50822.500000	2010.000000	NaN
max	479.000000	64.000000	999435.000000	2000.000000	2047.590000	1.000000e+07	620962.000000	100500.000000	0.000000	23.000000	4.00000	2.000000	3.000000	114920.00000	21450.000000	23670.000000	79560.000000	2015.000000	NaN

	months_as_customer	age	policy_number	policy_deductable	policy_annual_premium	umbrella_limit	capital-gains	capital-loss	incident_hour_of_the_day	number_of_vehicles_involved	bodily_injuries	witnesses	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_year	fraud_reported	_c39
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1.000000e+03	1000.000000	1000.000000	1000.000000	1000.00000	1000.000000	1000.000000	1000.00000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	0.0
mean	203.954000	38.948000	546238.648000	1136.000000	1256.406150	1.101000e+06	25126.100000	-26793.700000	11.644000	1.83900	0.992000	1.487000	52761.94000	7433.420000	7399.570000	37928.950000	2005.103000	0.247000	NaN
std	115.113174	9.140287	257063.005276	611.864673	244.167395	2.297407e+06	27872.187708	28104.096686	6.951373	1.01888	0.820127	1.111335	26401.53319	4880.951853	4824.726179	18886.252893	6.015861	0.431483	NaN
min	0.000000	19.000000	100804.000000	500.000000	433.330000	-1.000000e+06	0.000000	-111100.000000	0.000000	1.00000	0.000000	0.000000	100.00000	0.000000	0.000000	70.000000	1995.000000	0.000000	NaN
25%	115.750000	32.000000	335980.250000	500.000000	1089.607500	0.000000e+00	0.000000	-51500.000000	6.000000	1.00000	0.000000	1.000000	41812.50000	4295.000000	4445.000000	30292.500000	2000.000000	0.000000	NaN
50%	199.500000	38.000000	533135.000000	1000.000000	1257.200000	0.000000e+00	0.000000	-23250.000000	12.000000	1.00000	1.000000	1.000000	58055.00000	6775.000000	6750.000000	42100.000000	2005.000000	0.000000	NaN
75%	276.250000	44.000000	759099.750000	2000.000000	1415.695000	0.000000e+00	51025.000000	0.000000	17.000000	3.00000	2.000000	2.000000	70592.50000	11305.000000	10885.000000	50822.500000	2010.000000	0.000000	NaN
max	479.000000	64.000000	999435.000000	2000.000000	2047.590000	1.000000e+07	100500.000000	0.000000	23.000000	4.00000	2.000000	3.000000	114920.00000	21450.000000	23670.000000	79560.000000	2015.000000	1.000000	NaN

	months_as_customer	age	policy_number	policy_bind_date	policy_state	policy_csl	policy_deductable	policy_annual_premium	umbrella_limit	insured_zip	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-gains	capital-loss	incident_date	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	incident_location	incident_hour_of_the_day	number_of_vehicles_involved	property_damage	bodily_injuries	witnesses	police_report_available	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_make	auto_model	auto_year	fraud_reported	_c39	incident_state_count
count	1000.000000	1000.000000	1000.000000	1000	1000	1000	1000.000000	1000.000000	1.000000e+03	1000.0	1000	1000	1000	1000	1000	1000.000000	1000.000000	1000	1000	1000	1000	1000	1000	1000	1000	1000.000000	1000.00000	1000	1000.000000	1000.000000	1000	1000.00000	1000.000000	1000.000000	1000.000000	1000	1000	1000.000000	1000.000000	0.0	1000.0
unique	NaN	NaN	NaN	951	3	3	NaN	NaN	NaN	995.0	2	7	14	20	6	NaN	NaN	60	4	4	4	5	7	7	1000	NaN	NaN	3	NaN	NaN	3	NaN	NaN	NaN	NaN	14	39	NaN	NaN	NaN	6.0
top	NaN	NaN	NaN	01-01-2006	OH	250/500	NaN	NaN	NaN	431202.0	FEMALE	JD	machine-op-inspct	reading	own-child	NaN	NaN	02-02-2015	Multi-vehicle Collision	Rear Collision	Minor Damage	Police	NY	Springfield	6435 Texas Ave	NaN	NaN	?	NaN	NaN	?	NaN	NaN	NaN	NaN	Suburu	RAM	NaN	NaN	NaN	262.0
freq	NaN	NaN	NaN	3	352	351	NaN	NaN	NaN	2.0	537	161	93	64	183	NaN	NaN	28	419	292	354	292	262	157	1	NaN	NaN	360	NaN	NaN	343	NaN	NaN	NaN	NaN	80	43	NaN	NaN	NaN	262.0
mean	203.954000	38.948000	546238.648000	NaN	NaN	NaN	1136.000000	1256.406150	1.101000e+06	NaN	NaN	NaN	NaN	NaN	NaN	25126.100000	-26793.700000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	11.644000	1.83900	NaN	0.992000	1.487000	NaN	52761.94000	7433.420000	7399.570000	37928.950000	NaN	NaN	2005.103000	0.247000	NaN	NaN
std	115.113174	9.140287	257063.005276	NaN	NaN	NaN	611.864673	244.167395	2.297407e+06	NaN	NaN	NaN	NaN	NaN	NaN	27872.187708	28104.096686	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.951373	1.01888	NaN	0.820127	1.111335	NaN	26401.53319	4880.951853	4824.726179	18886.252893	NaN	NaN	6.015861	0.431483	NaN	NaN
min	0.000000	19.000000	100804.000000	NaN	NaN	NaN	500.000000	433.330000	-1.000000e+06	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	-111100.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	1.00000	NaN	0.000000	0.000000	NaN	100.00000	0.000000	0.000000	70.000000	NaN	NaN	1995.000000	0.000000	NaN	NaN
25%	115.750000	32.000000	335980.250000	NaN	NaN	NaN	500.000000	1089.607500	0.000000e+00	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	-51500.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.000000	1.00000	NaN	0.000000	1.000000	NaN	41812.50000	4295.000000	4445.000000	30292.500000	NaN	NaN	2000.000000	0.000000	NaN	NaN
50%	199.500000	38.000000	533135.000000	NaN	NaN	NaN	1000.000000	1257.200000	0.000000e+00	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	-23250.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12.000000	1.00000	NaN	1.000000	1.000000	NaN	58055.00000	6775.000000	6750.000000	42100.000000	NaN	NaN	2005.000000	0.000000	NaN	NaN
75%	276.250000	44.000000	759099.750000	NaN	NaN	NaN	2000.000000	1415.695000	0.000000e+00	NaN	NaN	NaN	NaN	NaN	NaN	51025.000000	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	17.000000	3.00000	NaN	2.000000	2.000000	NaN	70592.50000	11305.000000	10885.000000	50822.500000	NaN	NaN	2010.000000	0.000000	NaN	NaN
max	479.000000	64.000000	999435.000000	NaN	NaN	NaN	2000.000000	2047.590000	1.000000e+07	NaN	NaN	NaN	NaN	NaN	NaN	100500.000000	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	23.000000	4.00000	NaN	2.000000	3.000000	NaN	114920.00000	21450.000000	23670.000000	79560.000000	NaN	NaN	2015.000000	1.000000	NaN	NaN

	months_as_customer	age	policy_deductable	policy_annual_premium	umbrella_limit	capital-gains	capital-loss	number_of_vehicles_involved	bodily_injuries	witnesses	total_claim_amount	injury_claim	property_claim	vehicle_claim	fraud_reported	vehicle_age
0	328	48	1000	1406.91	0	53300	0	1	1	2	71610	6510	13020	52080	1	14
1	228	42	2000	1197.22	5000000	0	0	1	0	0	5070	780	780	3510	1	11
2	134	29	2000	1413.14	5000000	35100	0	3	2	3	34650	7700	3850	23100	0	11
3	256	41	2000	1415.74	6000000	48900	-62400	1	1	2	63400	6340	6340	50720	1	4
4	228	44	1000	1583.91	6000000	66000	-46000	1	0	1	6500	1300	650	4550	0	9

Auto Insurance Claims Fraud Detection¶

Business Requirement¶

Data Processing¶

Label encoding¶

We now have a dataset that we could use to evaluate an algorithm sensitive to missing values like LDA.¶

Random Forest Classification¶

	incident_hour_of_the_day	incident_period_of_day
0	5	early_morning
1	8	morning
2	7	morning
3	5	early_morning
4	20	evening
5	19	evening
6	0	past_midnight
7	23	night
8	21	night
9	14	afternoon
10	22	night
11	21	night
12	9	morning
13	5	early_morning
14	12	fore-noon
15	12	fore-noon
16	0	past_midnight
17	9	morning
18	19	evening
19	8	morning

	policy_state_IL	policy_state_IN	policy_state_OH	insured_sex_FEMALE	insured_sex_MALE	insured_education_level_Associate	insured_education_level_College	insured_education_level_High School	insured_education_level_JD	insured_education_level_MD	insured_education_level_Masters	insured_education_level_PhD	insured_occupation_adm-clerical	insured_occupation_armed-forces	insured_occupation_craft-repair	insured_occupation_exec-managerial	insured_occupation_farming-fishing	insured_occupation_handlers-cleaners	insured_occupation_machine-op-inspct	insured_occupation_other-service	insured_occupation_priv-house-serv	insured_occupation_prof-specialty	insured_occupation_protective-serv	insured_occupation_sales	insured_occupation_tech-support	insured_occupation_transport-moving	insured_hobbies_base-jumping	insured_hobbies_basketball	insured_hobbies_board-games	insured_hobbies_bungie-jumping	insured_hobbies_camping	insured_hobbies_chess	insured_hobbies_cross-fit	insured_hobbies_dancing	insured_hobbies_exercise	insured_hobbies_golf	insured_hobbies_hiking	insured_hobbies_kayaking	insured_hobbies_movies	insured_hobbies_paintball	insured_hobbies_polo	insured_hobbies_reading	insured_hobbies_skydiving	insured_hobbies_sleeping	insured_hobbies_video-games	insured_hobbies_yachting	insured_relationship_husband	insured_relationship_not-in-family	insured_relationship_other-relative	insured_relationship_own-child	insured_relationship_unmarried	insured_relationship_wife	incident_type_Multi-vehicle Collision	incident_type_Parked Car	incident_type_Single Vehicle Collision	incident_type_Vehicle Theft	incident_severity_Major Damage	incident_severity_Minor Damage	incident_severity_Total Loss	incident_severity_Trivial Damage	authorities_contacted_Ambulance	authorities_contacted_Fire	authorities_contacted_None	authorities_contacted_Other	authorities_contacted_Police	incident_state_NC	incident_state_NY	incident_state_OH	incident_state_PA	incident_state_SC	incident_state_VA	incident_state_WV	incident_city_Arlington	incident_city_Columbus	incident_city_Hillsdale	incident_city_Northbend	incident_city_Northbrook	incident_city_Riverwood	incident_city_Springfield	auto_make_Accura	auto_make_Audi	auto_make_BMW	auto_make_Chevrolet	auto_make_Dodge	auto_make_Ford	auto_make_Honda	auto_make_Jeep	auto_make_Mercedes	auto_make_Nissan	auto_make_Saab	auto_make_Suburu	auto_make_Toyota	auto_make_Volkswagen	auto_model_3 Series	auto_model_92x	auto_model_93	auto_model_95	auto_model_A3	auto_model_A5	auto_model_Accord	auto_model_C300	auto_model_CRV	auto_model_Camry	auto_model_Civic	auto_model_Corolla	auto_model_E400	auto_model_Escape	auto_model_F150	auto_model_Forrestor	auto_model_Fusion	auto_model_Grand Cherokee	auto_model_Highlander	auto_model_Impreza	auto_model_Jetta	auto_model_Legacy	auto_model_M5	auto_model_MDX	auto_model_ML350	auto_model_Malibu	auto_model_Maxima	auto_model_Neon	auto_model_Passat	auto_model_Pathfinder	auto_model_RAM	auto_model_RSX	auto_model_Silverado	auto_model_TL	auto_model_Tahoe	auto_model_Ultima	auto_model_Wrangler	auto_model_X5	auto_model_X6	csl_per_person_100	csl_per_person_250	csl_per_person_500	csl_per_accident_1000	csl_per_accident_300	csl_per_accident_500	incident_period_of_day_afternoon	incident_period_of_day_early_morning	incident_period_of_day_evening	incident_period_of_day_fore-noon	incident_period_of_day_morning	incident_period_of_day_night	incident_period_of_day_past_midnight	property_damage	police_report_available	collision_en
0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	1	3
1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0

	policy_state_IL	policy_state_IN	policy_state_OH	insured_sex_FEMALE	insured_sex_MALE	insured_education_level_Associate	insured_education_level_College	insured_education_level_High School	insured_education_level_JD	insured_education_level_MD	insured_education_level_Masters	insured_education_level_PhD	insured_occupation_adm-clerical	insured_occupation_armed-forces	insured_occupation_craft-repair	insured_occupation_exec-managerial	insured_occupation_farming-fishing	insured_occupation_handlers-cleaners	insured_occupation_machine-op-inspct	insured_occupation_other-service	insured_occupation_priv-house-serv	insured_occupation_prof-specialty	insured_occupation_protective-serv	insured_occupation_sales	insured_occupation_tech-support	insured_occupation_transport-moving	insured_hobbies_base-jumping	insured_hobbies_basketball	insured_hobbies_board-games	insured_hobbies_bungie-jumping	insured_hobbies_camping	insured_hobbies_chess	insured_hobbies_cross-fit	insured_hobbies_dancing	insured_hobbies_exercise	insured_hobbies_golf	insured_hobbies_hiking	insured_hobbies_kayaking	insured_hobbies_movies	insured_hobbies_paintball	insured_hobbies_polo	insured_hobbies_reading	insured_hobbies_skydiving	insured_hobbies_sleeping	insured_hobbies_video-games	insured_hobbies_yachting	insured_relationship_husband	insured_relationship_not-in-family	insured_relationship_other-relative	insured_relationship_own-child	insured_relationship_unmarried	insured_relationship_wife	incident_type_Multi-vehicle Collision	incident_type_Parked Car	incident_type_Single Vehicle Collision	incident_type_Vehicle Theft	incident_severity_Major Damage	incident_severity_Minor Damage	incident_severity_Total Loss	incident_severity_Trivial Damage	authorities_contacted_Ambulance	authorities_contacted_Fire	authorities_contacted_None	authorities_contacted_Other	authorities_contacted_Police	incident_state_NC	incident_state_NY	incident_state_OH	incident_state_PA	incident_state_SC	incident_state_VA	incident_state_WV	incident_city_Arlington	incident_city_Columbus	incident_city_Hillsdale	incident_city_Northbend	incident_city_Northbrook	incident_city_Riverwood	incident_city_Springfield	auto_make_Accura	auto_make_Audi	auto_make_BMW	auto_make_Chevrolet	auto_make_Dodge	auto_make_Ford	auto_make_Honda	auto_make_Jeep	auto_make_Mercedes	auto_make_Nissan	auto_make_Saab	auto_make_Suburu	auto_make_Toyota	auto_make_Volkswagen	auto_model_3 Series	auto_model_92x	auto_model_93	auto_model_95	auto_model_A3	auto_model_A5	auto_model_Accord	auto_model_C300	auto_model_CRV	auto_model_Camry	auto_model_Civic	auto_model_Corolla	auto_model_E400	auto_model_Escape	auto_model_F150	auto_model_Forrestor	auto_model_Fusion	auto_model_Grand Cherokee	auto_model_Highlander	auto_model_Impreza	auto_model_Jetta	auto_model_Legacy	auto_model_M5	auto_model_MDX	auto_model_ML350	auto_model_Malibu	auto_model_Maxima	auto_model_Neon	auto_model_Passat	auto_model_Pathfinder	auto_model_RAM	auto_model_RSX	auto_model_Silverado	auto_model_TL	auto_model_Tahoe	auto_model_Ultima	auto_model_Wrangler	auto_model_X5	auto_model_X6	csl_per_person_100	csl_per_person_250	csl_per_person_500	csl_per_accident_1000	csl_per_accident_300	csl_per_accident_500	incident_period_of_day_afternoon	incident_period_of_day_early_morning	incident_period_of_day_evening	incident_period_of_day_fore-noon	incident_period_of_day_morning	incident_period_of_day_night	incident_period_of_day_past_midnight	property_damage	police_report_available	collision_en	months_as_customer	age	policy_deductable	policy_annual_premium	umbrella_limit	capital-gains	capital-loss	number_of_vehicles_involved	bodily_injuries	witnesses	total_claim_amount	injury_claim	property_claim	vehicle_claim	vehicle_age
0	0.0	0.0	2.088161	0.0	2.006033	0.0	0.0	2.685971	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.660072	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.639399	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	2.624446	0.0	0.0	0.0	0.000000	0.0	2.044498	0.0	0.0	2.083333	0.0	0.0	2.536374	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	3.262074	0.0	0.00000	0.0	2.860721	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.686049	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	5.441296	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.086534	0.0	0.0	0.0	2.086534	0.0	3.023716	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.793297	0.548299	3.132848	3.280121	4.926916	0.0	0.0000	-2.171862	0.980864	1.229787	1.799726	1.644157	0.901702	0.903754	1.840932	1.814304
1	0.0	0.0	2.088161	0.0	2.006033	0.0	0.0	2.685971	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.660072	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	4.386345	0.0	0.0	0.0	0.0	0.0	0.0	2.685971	0.0	0.000000	0.0	0.0	0.0	2.026102	0.0	0.000000	0.0	0.0	2.083333	0.0	0.0	2.536374	0.0	0.0	0.0	0.0	3.333333	0.0	0.0	0.0	0.0	0.000000	0.0	2.80056	0.0	0.000000	0.0	0.0	0.0	0.0	3.826087	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.181386	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.086534	0.0	0.0	0.0	2.086534	0.0	3.023716	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.793297	1.173702	3.348906	3.280121	5.493052	0.0	1.8834	-1.436117	2.942591	1.229787	1.799726	1.911286	2.096404	2.101175	1.605023	2.144178

	policy_state_IL	policy_state_IN	policy_state_OH	insured_sex_FEMALE	insured_sex_MALE	insured_education_level_Associate	insured_education_level_College	insured_education_level_High School	insured_education_level_JD	insured_education_level_MD	insured_education_level_Masters	insured_education_level_PhD	insured_occupation_adm-clerical	insured_occupation_armed-forces	insured_occupation_craft-repair	insured_occupation_exec-managerial	insured_occupation_farming-fishing	insured_occupation_handlers-cleaners	insured_occupation_machine-op-inspct	insured_occupation_other-service	insured_occupation_priv-house-serv	insured_occupation_prof-specialty	insured_occupation_protective-serv	insured_occupation_sales	insured_occupation_tech-support	insured_occupation_transport-moving	insured_hobbies_base-jumping	insured_hobbies_basketball	insured_hobbies_board-games	insured_hobbies_bungie-jumping	insured_hobbies_camping	insured_hobbies_chess	insured_hobbies_cross-fit	insured_hobbies_dancing	insured_hobbies_exercise	insured_hobbies_golf	insured_hobbies_hiking	insured_hobbies_kayaking	insured_hobbies_movies	insured_hobbies_paintball	insured_hobbies_polo	insured_hobbies_reading	insured_hobbies_skydiving	insured_hobbies_sleeping	insured_hobbies_video-games	insured_hobbies_yachting	insured_relationship_husband	insured_relationship_not-in-family	insured_relationship_other-relative	insured_relationship_own-child	insured_relationship_unmarried	insured_relationship_wife	incident_type_Multi-vehicle Collision	incident_type_Parked Car	incident_type_Single Vehicle Collision	incident_type_Vehicle Theft	incident_severity_Major Damage	incident_severity_Minor Damage	incident_severity_Total Loss	incident_severity_Trivial Damage	authorities_contacted_Ambulance	authorities_contacted_Fire	authorities_contacted_None	authorities_contacted_Other	authorities_contacted_Police	incident_state_NC	incident_state_NY	incident_state_OH	incident_state_PA	incident_state_SC	incident_state_VA	incident_state_WV	incident_city_Arlington	incident_city_Columbus	incident_city_Hillsdale	incident_city_Northbend	incident_city_Northbrook	incident_city_Riverwood	incident_city_Springfield	auto_make_Accura	auto_make_Audi	auto_make_BMW	auto_make_Chevrolet	auto_make_Dodge	auto_make_Ford	auto_make_Honda	auto_make_Jeep	auto_make_Mercedes	auto_make_Nissan	auto_make_Saab	auto_make_Suburu	auto_make_Toyota	auto_make_Volkswagen	auto_model_3 Series	auto_model_92x	auto_model_93	auto_model_95	auto_model_A3	auto_model_A5	auto_model_Accord	auto_model_C300	auto_model_CRV	auto_model_Camry	auto_model_Civic	auto_model_Corolla	auto_model_E400	auto_model_Escape	auto_model_F150	auto_model_Forrestor	auto_model_Fusion	auto_model_Grand Cherokee	auto_model_Highlander	auto_model_Impreza	auto_model_Jetta	auto_model_Legacy	auto_model_M5	auto_model_MDX	auto_model_ML350	auto_model_Malibu	auto_model_Maxima	auto_model_Neon	auto_model_Passat	auto_model_Pathfinder	auto_model_RAM	auto_model_RSX	auto_model_Silverado	auto_model_TL	auto_model_Tahoe	auto_model_Ultima	auto_model_Wrangler	auto_model_X5	auto_model_X6	csl_per_person_100	csl_per_person_250	csl_per_person_500	csl_per_accident_1000	csl_per_accident_300	csl_per_accident_500	incident_period_of_day_afternoon	incident_period_of_day_early_morning	incident_period_of_day_evening	incident_period_of_day_fore-noon	incident_period_of_day_morning	incident_period_of_day_night	incident_period_of_day_past_midnight	property_damage	police_report_available	collision_en
0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	1	3
1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0

	policy_state_IL	policy_state_IN	policy_state_OH	insured_sex_FEMALE	insured_sex_MALE	insured_education_level_Associate	insured_education_level_College	insured_education_level_High School	insured_education_level_JD	insured_education_level_MD	insured_education_level_Masters	insured_education_level_PhD	insured_occupation_adm-clerical	insured_occupation_armed-forces	insured_occupation_craft-repair	insured_occupation_exec-managerial	insured_occupation_farming-fishing	insured_occupation_handlers-cleaners	insured_occupation_machine-op-inspct	insured_occupation_other-service	insured_occupation_priv-house-serv	insured_occupation_prof-specialty	insured_occupation_protective-serv	insured_occupation_sales	insured_occupation_tech-support	insured_occupation_transport-moving	insured_hobbies_base-jumping	insured_hobbies_basketball	insured_hobbies_board-games	insured_hobbies_bungie-jumping	insured_hobbies_camping	insured_hobbies_chess	insured_hobbies_cross-fit	insured_hobbies_dancing	insured_hobbies_exercise	insured_hobbies_golf	insured_hobbies_hiking	insured_hobbies_kayaking	insured_hobbies_movies	insured_hobbies_paintball	insured_hobbies_polo	insured_hobbies_reading	insured_hobbies_skydiving	insured_hobbies_sleeping	insured_hobbies_video-games	insured_hobbies_yachting	insured_relationship_husband	insured_relationship_not-in-family	insured_relationship_other-relative	insured_relationship_own-child	insured_relationship_unmarried	insured_relationship_wife	incident_type_Multi-vehicle Collision	incident_type_Parked Car	incident_type_Single Vehicle Collision	incident_type_Vehicle Theft	incident_severity_Major Damage	incident_severity_Minor Damage	incident_severity_Total Loss	incident_severity_Trivial Damage	authorities_contacted_Ambulance	authorities_contacted_Fire	authorities_contacted_None	authorities_contacted_Other	authorities_contacted_Police	incident_state_NC	incident_state_NY	incident_state_OH	incident_state_PA	incident_state_SC	incident_state_VA	incident_state_WV	incident_city_Arlington	incident_city_Columbus	incident_city_Hillsdale	incident_city_Northbend	incident_city_Northbrook	incident_city_Riverwood	incident_city_Springfield	auto_make_Accura	auto_make_Audi	auto_make_BMW	auto_make_Chevrolet	auto_make_Dodge	auto_make_Ford	auto_make_Honda	auto_make_Jeep	auto_make_Mercedes	auto_make_Nissan	auto_make_Saab	auto_make_Suburu	auto_make_Toyota	auto_make_Volkswagen	auto_model_3 Series	auto_model_92x	auto_model_93	auto_model_95	auto_model_A3	auto_model_A5	auto_model_Accord	auto_model_C300	auto_model_CRV	auto_model_Camry	auto_model_Civic	auto_model_Corolla	auto_model_E400	auto_model_Escape	auto_model_F150	auto_model_Forrestor	auto_model_Fusion	auto_model_Grand Cherokee	auto_model_Highlander	auto_model_Impreza	auto_model_Jetta	auto_model_Legacy	auto_model_M5	auto_model_MDX	auto_model_ML350	auto_model_Malibu	auto_model_Maxima	auto_model_Neon	auto_model_Passat	auto_model_Pathfinder	auto_model_RAM	auto_model_RSX	auto_model_Silverado	auto_model_TL	auto_model_Tahoe	auto_model_Ultima	auto_model_Wrangler	auto_model_X5	auto_model_X6	csl_per_person_100	csl_per_person_250	csl_per_person_500	csl_per_accident_1000	csl_per_accident_300	csl_per_accident_500	incident_period_of_day_afternoon	incident_period_of_day_early_morning	incident_period_of_day_evening	incident_period_of_day_fore-noon	incident_period_of_day_morning	incident_period_of_day_night	incident_period_of_day_past_midnight	property_damage	police_report_available	collision_en
0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	1	3
1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0